import sys
import numpy as np
import pandas as pd
import seaborn as sns
sns.set_theme()

results_folder = 'mmvec_major_taxa'
results_base_name = 'latent_dim_3_input_prior_1.00_output_prior_1.00_beta1_0.90_beta2_0.95'

table = pd.read_table(results_folder + '/' + results_base_name + '_ranks.txt', index_col=0)
table.head()

	Propionibacteriaceae	Staphylococcus caprae or capitis	Staphylococcus epidermidis	Staphylococcus hominis	Other Staphylococci	Polyomavirus HPyV6	Polyomavirus HPyV7	Merkel Cell Polyomavirus	Malasseziaceae	Corynebacteriaceae	Micrococcaceae	Other families
featureid
X940001	0.177989	0.154912	0.095406	0.012139	0.145922	0.043939	-0.043831	0.105463	0.016188	0.313228	0.165941	0.045453
X940002	0.006612	-0.042924	-0.242801	-0.026591	-0.004430	-0.062433	-0.163995	-0.130395	0.064656	-0.058323	-0.115458	-0.112474
X940005	-0.029823	-0.143696	-0.292455	-0.234061	0.057963	0.406687	-0.044387	-0.068616	0.037003	-0.125615	-0.106984	-0.063329
X940007	0.461745	0.421363	0.296187	0.570376	0.372838	0.035342	0.238131	0.304312	0.476247	0.427392	0.348371	0.321372
X940010	0.408488	0.030971	0.778209	0.523698	0.344422	0.420056	0.744065	0.699084	0.151153	0.408506	0.632428	0.631959

#table['Selected'] = np.logical_and(np.logical_and(table['Propionibacteriaceae']<0.4, table['Staphylococcus epidermidis']>0.8), table['Propionibacteriaceae'] - table['Staphylococcus epidermidis']<-1)
table['Selected'] = np.isin(table.index,
                            ['X940203', 'X940589', 'X940625', 'X940925', 'X940936', 'X942191',
                             'X942237', 'X950023', 'X950028', 'X950056', 'X950157', 'X950173',
                             'X950193', 'X950225', 'X950228', 'X950233', 'X950254', 'X950396',
                             'X950485', 'X950584', 'X950661', 'X950999', 'X960035', 'X960242',
                             'X960306', 'X960421', 'X960463', 'X960465', 'X960712', 'X960726',
                             'X960934', 'X961553', 'X961686', 'X970018', 'X970091', 'X970092',
                             'X970232', 'X970283', 'X970327', 'X970342', 'X970633', 'X970680']
                           )
table.sort_values('Selected', inplace=True)
sns.relplot(
    table,
    y='Propionibacteriaceae', x='Staphylococcus epidermidis', hue='Selected'
)

<seaborn.axisgrid.FacetGrid at 0x7fc773407f90>

The three molecular families from version 1 of the manuscript were X950225, X950396, X960242. All three are in the set of Selecteded molecular families from, the new analysis :-)

sns.pairplot(table, hue='Selected')

<seaborn.axisgrid.PairGrid at 0x7fc772db7f50>

for i in table.columns[:-1]:
    sns.displot(table, x=i, hue='Selected', multiple='stack')